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Analysis of the Spatio-temporal Dynamics of COVID-19 in Massachusetts via Spectral Graph Wavelet Theory

arXiv 2208.01749 ↗First posted 28 Jul 2022cs.SI cs.LG physics.soc-phcited by 3

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Abstract (arXiv)

The rapid spread of COVID-19 disease has had a significant impact on the world. In this paper, we study COVID-19 data interpretation and visualization using open-data sources for 351 cities and towns in Massachusetts from December 6, 2020 to September 25, 2021. Because cities are embedded in rather complex transportation networks, we construct the spatio-temporal dynamic graph model, in which the graph attention neural network is utilized as a deep learning method to learn the pandemic transition probability among major cities in Massachusetts. Using the spectral graph wavelet transform (SGWT), we process the COVID-19 data on the dynamic graph, which enables us to design effective tools to analyze and detect spatio-temporal patterns in the pandemic spreading. We design a new node classification method, which effectively identifies the anomaly cities based on spectral graph wavelet coefficients. It can assist administrations or public health organizations in monitoring the spread of the pandemic and developing preventive measures. Unlike most work focusing on the evolution of confirmed cases over time, we focus on the spatio-temporal patterns of pandemic evolution among cities. Through the data analysis and visualization, a better understanding of the epidemiological development at the city level is obtained and can be helpful with city-specific surveillance.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 8 of its 47 references that are in the map. First posted on arXiv 28 Jul 2022.

Builds on

8 of 47 references.

References · 8

  1. 2017Attention Is All You Needcited by 59,932
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  3. 2020Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depthcited by 241
  4. 2020Examining COVID-19 Forecasting using Spatio-Temporal Graph Neural Networkscited by 64
  5. 2013Spectrum-Adapted Tight Graph Wavelet and Vertex-Frequency Framescited by 15
  6. 2019Link Prediction via Graph Attention Networkcited by 4
  7. 2020Epidemic Dynamics via Wavelet Theory and Machine Learning, with Applications to Covid-19cited by 2
  8. 2021The Interplay of Demographic Variables and Social Distancing Scores in Deep Prediction of U.S. COVID-19 Casescited by 2

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Cited by year

Citing papers per year: 2022: 2; 2023: 1.

3 papers.

  1. 2023A Survey on Graph Neural Networks for Time Series: Forecasting, Classification, Imputation, and Anomaly Detectioncited by 145
  2. 2022A Modified PINN Approach for Identifiable Compartmental Models in Epidemiology with Applications to COVID-19cited by 3
  3. 2022Attributed Network Embedding Model for Exposing COVID-19 Spread Trajectory Archetypescited by 1